空间层次聚类显著性判别的重排检验方法

同时顾及空间邻近与专题属性相似的空间层次聚类是挖掘空间分布模式的一种有效手段。空间层次聚类方法虽然可以获得多层次的聚集结构,但聚类结果显著性的统计判别依然是一个尚未解决的难题。为此,本文提出了一种空间层次聚类结果显著性的统计判别方法,用于确定空间层次聚类的停止准则,减少聚类过程对参数设置的依赖。通过试验分析与比较发现,该方法能够有效判别空间层次聚类结果的显著性和确定层次聚类合并过程的停止条件,同时具有很好的抗噪性,避免随机结构的干扰。     

现代专题地图制图研究进展与趋势分析

针对专题地图制作相关研究不足的问题,该文通过归纳总结专题地图理论与应用、网络共享等方面的研究进展,从功能转变、模式转变和内涵冲击三方面分析专题地图研究趋势,并探讨现有制图理论技术存在的不足之处。文章认为:研究专题地图符号的内在结构及统一构建方式,在符号学和微观语言学的理论框架下建立专题符号句法模式,注重对专题地图符号模型的整体描述,并通过相应制图模型的研究来优化制图过程,发掘和创新制图理论成果,将会促进专题地图理论的完善,深化专题符号的自适应表达。     

“GIS工程设计”的实验教学模式研究

"GIS工程设计"的教学目的是培养学生GIS设计的综合能力,因此,该课程的实践教学很重要。本文提出将"GIS工程设计"的实践教学体系分为基础实验和综合设计两部分,研究实践教学安排,改善实习的考核体系和辅助教学方法,形成一套高效的实践教学模式。     

Grid pattern recognition in road networks using the C4.5 algorithm

Pattern recognition in road networks can be used for different applications, including spatiotemporal data mining, automated map generalization, data matching of different levels of detail, and other important research topics. Grid patterns are a common pattern type. This paper proposes and implements a method for grid pattern recognition based on the idea of mesh classification through a supervised learning process. To train the classifier, training datasets are selected from worldwide city samples with different cultural, historical, and geographical environments. Meshes are subsequently labeled as composing or noncomposing grids by participants in an experiment, and the mesh measures are defined while accounting for the mesh’s individual characteristics and spatial context. The classifier is generated using the C4.5 algorithm. The accuracy of the classifier is evaluated using Kappa statistics and the overall rate of correctness. The average Kappa value is approximately 0.74, which corresponds to a total accuracy of 87.5%. Additionally, the rationality of the classifier is evaluated in an interpretation step. Two other existing grid pattern recognition methods were also tested on the datasets, and comparison results indicate that our approach is effective in identifying grid patterns in road networks.     

Forest fragmentation in Massachusetts,USA: a town-level assessment using Morphological spatial pattern analysis and affinity propagation

Forest fragmentation has been studied extensively with respect to biodiversity loss, disruption of ecosystem services, and edge effects although the relationship between forest fragmentation and human activities is still not well understood. We classified the pattern of forests in Massachusetts using fragmentation indicators to address these objectives: 1) characterize the spatial pattern of forest fragmentation in Massachusetts towns using Morphological Spatial Pattern Analysis (MSPA); and (2) identify regional trends using archetypal towns in relation to town history, geography and socioeconomic characteristics. Six fragmentation indicators were calculated using MSPA for each town to represent patterns and processes of fragmentation. We then used these indicators and the proportion of forested land to group towns across Massachusetts with similar patterns of fragmentation. Six representative towns typify different types of forest fragmentation, and illustrate the commonalities and differences between different fragmentation types. The objective selection of representative towns suggests that they might be used as the target of future studies, both in retrospective studies that seek to explain current patterns and in analyses that predict future fragmentation trends.     

秋季南黄海浮游动物分布及其影响因素

基于2007年秋季在南黄海（32°20'~37°00'N;124°E以西）进行的浮游动物及环境因子大面调查;分析了秋季南黄海浮游动物种类组成、分布特征及其影响因素;主要结果如下:共鉴定浮游动物113种（不包括25种浮游幼体）;中华哲水蚤（Calanus sinicus）、强壮滨箭虫（Aidanosagitta crassa）、磷虾幼体（Euphausia larvae）和小齿海樽（Doliolum denticulatum）是秋季优势种;浮游动物丰度为（156.37±12.04）ind/m3;生物量为（172.57±10.41）mg/m3;与历史调查数据相比;本航次浮游动物丰度和生物量相对处于较高水平;磷虾幼体分布趋势与中华假磷虾（Psudeuphausia sinica）一致;说明秋季是中华假磷虾种群的一个重要的补充时期;小齿海樽在南黄海的大量出现系自身种群补充的结果;精致真刺水蚤（Euchaeta concinna）和肥胖软箭虫（Flaccisagitta enflata）主要分布在深水区;在近岸海区很少出现。中华哲水蚤、强壮滨箭虫丰度高值区倾向分布于海洋锋附近;进一步佐证了海洋锋对浮游动物的积聚作用。     

50年来北海市滨海湿地景观格局变化及其驱动机制

以1955~2004年遥感影像为数据源,结合野外调查,运用景观生态学原理,研究50 a间北海市滨海湿地的景观格局及其动态变化。结果表明,50 a间北海市滨海湿地呈整体退化趋势,潮间砂质海滩、潮间淤泥质海滩、红树林滩、小型岛屿湿地减少;景观格局指数显示1977年、1998年是北海市滨海湿地较明显变化的拐点。1977年人类活动影响增加,滨海湿地稍有退化,湿地景观多样性指数减小,优势度指数增加,均匀度指数减小、景观总面积指数降至最低值。1988年湿地保护力度加大,湿地略为好转,景观多样性指数、优势度指数、均匀度指数则呈相反规律。1998年湿地受到的极端气候灾害、人类干扰程度加大,平均斑块分形维数下降,斑块密度指数、景观斑块数破碎化指数呈最高值。湿地景观格局指数的变化,反映了围垦改造、互花米草的生物入侵、城市污染、极端气候灾害等对景观格局的影响,人类活动是北海市滨海湿地景观格局变化的主要驱动因子。     

Unsupervised regionalization of the United States into landscape pattern types

We present a pattern-based regionalization of the conterminous US – a partitioning of the country into a number of mutually exclusive and exhaustive regions that maximizes the intra-region stationarity of land cover patterns and inter-region disparity between those patterns. The result is a discretization of the land surface into a number of landscape pattern types (LPTs) – spatial units each containing a unique quasi-stationary pattern of land cover classes. To achieve this goal, we use a recently developed method which utilizes machine vision techniques. First, the entire National Land Cover Dataset (NLCD) is partitioned into a grid of square-size blocks of cells, called motifels. The size of a motifel defines the spatial scale of a local landscape. The land cover classes of cells within a motifel form a local landscape pattern which is mathematically represented by a histogram of co-occurrence features. Using the Jensen–Shannon divergence as a dissimilarity function between patterns we group the motifels into several LPTs. The grouping procedure consists of two phases. First, the grid of motifels is partitioned spatially using a region-growing segmentation algorithm. Then, the resulting segments of this grid, each represented by its medoid, are clustered using a hierarchical algorithm with Ward’s linkage. The broad-extent maps of progressively more generalized LPTs resulting from this procedure are shown and discussed. Our delineated LPTs agree well with the perceptual patterns seen in the NLCD map.     

Interactive discovery of sequential patterns in time series of wind data

Wind speed and direction vary over space and time due to the interactions between different pressures and temperature gradients within the atmospheric layers. Near the earth’s surface, these interactions are modulated by topography and artificial structures. Hence, characterizing wind behaviour over large areas and long periods is a complex but essential task for various energy-related applications. In this study, we present a novel approach to discover wind patterns by integrating sequential pattern mining and interactive visualization techniques. The approach relies on the use of the Linear time Closed pattern Miner sequence algorithm in conjunction with a time sliding window that allows the discovery of all sequential patterns present in the data. These patterns are then visualized using integrated 2D and 3D coordinated multiple views and visually explored to gain insight into the characteristics of the wind from a spatial, temporal and attribute (type of wind pattern) point of view. This proposed approach is used to analyse 10 years of hourly wind speed and direction data for 29 weather stations in the Netherlands. The results show that there are 15 main sequential patterns in the data. The spatial task shows that weather stations located in the same region do not necessarily experience similar wind pattern. For within the selected time interval, similar wind patterns can be observed in different stations and in the same station at different times of occurrence. The attribute task discovered that the repetitive occurrences of chosen pattern indicate as regular wind behaviour at different weather stations that persisted continuously over time. The results of these tasks show that the proposed interactive discovery facilitates the understanding of wind dynamics in space and time.